Hyp3rArmor: reducing web application exposure to automated attacks

Web applications (webapps) are subjected constantly to automated, opportunistic attacks from autonomous robots (bots) engaged in reconnaissance to discover victims that may be vulnerable to specific exploits. This is a typical behavior found in botnet recruitment, worm propagation, largescale fingerprinting and vulnerability scanners. Most anti-bot techniques are deployed at the application layer, thus leaving the network stack of the webapp’s server exposed. In this paper we present a mechanism called Hyp3rArmor, that addresses this vulnerability by minimizing the webapp’s attack surface exposed to automated opportunistic attackers, for JavaScriptenabled web browser clients. Our solution uses port knocking to eliminate the webapp’s visible network footprint. Clients of the webapp are directed to a visible static web server to obtain JavaScript that authenticates the client to the webapp server (using port knocking) before making any requests to the webapp. Our implementation of Hyp3rArmor, which is compatible with all webapp architectures, has been deployed and used to defend single and multi-page websites on the Internet for 114 days. During this time period the static web server observed 964 attempted attacks that were deflected from the webapp, which was only accessed by authenticated clients. Our evaluation shows that in most cases client-side overheads were negligible and that server-side overheads were minimal. Hyp3rArmor is ideal for critical systems and legacy applications that must be accessible on the Internet. Additionally Hyp3rArmor is composable with other security tools, adding an additional layer to a defense in depth approach.This work has been supported by the National Science Foundation (NSF) awards #1430145, #1414119, and #1012798

Survey on intrusion detection systems based on deep learning

Intrusion Detection Systems (IDSs) have a significant role in all networks and information systems in the world to earn the required security guarantee. IDS is one of the solutions used to reduce malicious attacks. As attackers always changing their techniques of attack and find alternative attack methods, IDS must also evolve in response by adopting more sophisticated methods of detection. The huge growth in the data and the significant advances in computer hardware technologies resulted in the new studies existence in the deep learning field, including intrusion detection. Deep learning is sub-field of Machine Learning (ML) methods that are based on learning data representations. In this paper, a detailed survey of various deep learning methods applied in IDSs is given first. Then, a deep learning classification scheme is presented and the main works that have been reported in the deep learning works is summarized. Utilizing this approach, we have provided a taxonomy survey on the available deep architectures and algorithms in these works and classify those algorithms to three classes, which are: discriminative, hybrid and generative. After that, chosen deep learning applications are reviewed in a wide range of fields of intrusion detection. Finally, popular types of datasets and frameworks are discussed

Are Public Intrusion Datasets Fit for Purpose: Characterising the State of the Art in Intrusion Event Datasets

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In recent years cybersecurity attacks have caused major disruption and information loss for online organisations, with high profile incidents in the news. One of the key challenges in advancing the state of the art in intrusion detection is the lack of representative datasets. These datasets typically contain millions of time-ordered events (e.g. network packet traces, flow summaries, log entries); subsequently analysed to identify abnormal behavior and specific attacks [1]. Generating realistic datasets has historically required expensive networked assets, specialised traffic generators, and considerable design preparation. Even with advances in virtualisation it remains challenging to create and maintain a representative environment. Major improvements are needed in the design, quality and availability of datasets, to assist researchers in developing advanced detection techniques. With the emergence of new technology paradigms, such as intelligent transport and autonomous vehicles, it is also likely that new classes of threat will emerge [2]. Given the rate of change in threat behavior [3] datasets become quickly obsolete, and some of the most widely cited datasets date back over two decades. Older datasets have limited value: often heavily filtered and anonymised, with unrealistic event distributions, and opaque design methodology. The relative scarcity of (Intrusion Detection System) IDS datasets is compounded by the lack of a central registry, and inconsistent information on provenance. Researchers may also find it hard to locate datasets or understand their relative merits. In addition, many datasets rely on simulation, originating from academic or government institutions. The publication process itself often creates conflicts, with the need to de-identify sensitive information in order to meet regulations such as General Data Protection Act (GDPR) [4]. Another final issue for researchers is the lack of standardised metrics with which to compare dataset quality. In this paper we attempt to classify the most widely used public intrusion datasets, providing references to archives and associated literature. We illustrate their relative utility and scope, highlighting the threat composition, formats, special features, and associated limitations. We identify best practice in dataset design, and describe potential pitfalls of designing anomaly detection techniques based on data that may be either inappropriate, or compromised due to unrealistic threat coverage. Such contributions as made in this paper is expected to facilitate continuous research and development for effectively combating the constantly evolving cyber threat landscape